标题： 用于大型语言模型中数据滥用检测的统计假设检验框架 显示英文标题

标题： A Statistical Hypothesis Testing Framework for Data Misappropriation Detection in Large Language Models

摘要： 大型语言模型（LLMs）近年来迅速获得了巨大的流行度。 然而，LLMs的训练引发了重大的隐私和法律问题，特别是关于在未经适当署名或授权的情况下将受版权保护的材料包含在其训练数据中，这属于数据不当使用的更广泛问题。 在本文中，我们关注数据不当使用检测的一个具体问题，即确定给定的LLM是否包含了由另一个LLM生成的数据。 为了解决这个问题，我们提出在受版权保护的训练数据中嵌入水印，并将数据不当使用的检测形式化为假设检验问题。 我们开发了一个通用的统计检验框架，构建了一个关键统计量，确定了最优拒绝阈值，并明确控制了第一类和第二类错误。 此外，我们建立了所提出的检验的渐近最优性性质，并通过大量的数值实验证明了其经验有效性。 显示英文摘要

摘要： Large Language Models (LLMs) are rapidly gaining enormous popularity in recent years. However, the training of LLMs has raised significant privacy and legal concerns, particularly regarding the inclusion of copyrighted materials in their training data without proper attribution or licensing, which falls under the broader issue of data misappropriation. In this article, we focus on a specific problem of data misappropriation detection, namely, to determine whether a given LLM has incorporated data generated by another LLM. To address this issue, we propose embedding watermarks into the copyrighted training data and formulating the detection of data misappropriation as a hypothesis testing problem. We develop a general statistical testing framework, construct a pivotal statistic, determine the optimal rejection threshold, and explicitly control the type I and type II errors. Furthermore, we establish the asymptotic optimality properties of the proposed tests, and demonstrate its empirical effectiveness through intensive numerical experiments.